Statistics of Gene Expression
Haichen Wei
Dec 3, 2020
Set Up
Clean up environment and load packages
# clean up the RStudio environment
rm(list = ls())
# load all packages
library(tidyverse)
library(mosaic)
library(DataComputing)18.11 Simple Graphics for Gene Expression
Narrow <-
NCI60 %>%
tidyr::gather(cellLine, expression, -Probe)
CellTypes <-
NCI60cells %>%
select(cellLine, tissue) %>%
mutate(cellLine = gsub("\\:",".", as.character(cellLine)))
Narrow <-
Narrow %>%
inner_join(CellTypes)Joining, by = "cellLine"NarrowProbe_TOP3A <-
Narrow %>%
filter(Probe=="TOP3A")
Probe_TOP3ASummaryStats <-
Probe_TOP3A %>%
group_by(tissue) %>%
summarise(mn_expr = exp(mean(expression, na.rm = TRUE)))
SummaryStats18.1
SummaryStats %>%
ggplot(aes(x = tissue, y = mn_expr)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
18.2
Probe_TOP3A %>%
ggplot(aes(x = tissue, y = exp(expression))) +
geom_point() +
theme(axis.text.x = element_text(angle = 30, hjust=1))
18.3
SummaryStats <-
SummaryStats %>%
mutate(tissue = reorder(tissue, mn_expr))
SummaryStats <-
Probe_TOP3A %>%
group_by(tissue) %>%
summarise(mn_expr = mean(expression, na.rm = TRUE),
se = sd(expression, na.rm = TRUE) / sqrt(n()))
SummaryStats %>%
ggplot(aes(x = tissue, y = exp(mn_expr))) +
geom_bar(stat = "identity", fill="gray", color=NA) +
geom_point(data = Probe_TOP3A, aes(x=tissue, y=exp(expression))) +
theme(axis.text.x = element_text(angle = 30, hjust=1))
18.4
SummaryStats <-
SummaryStats %>%
mutate(top = mn_expr + 2 * se,
bottom = mn_expr - 2 * se)
SummaryStats %>%
ggplot(aes(x = tissue, y = exp(mn_expr))) +
geom_bar(stat = "identity", alpha=0.2) +
geom_errorbar(aes(x = tissue,
ymax = exp(top),
ymin =exp(bottom)), width=0.5) +
theme(axis.text.x = element_text(angle = 30, hjust=1))
18.5
SummaryStats %>%
ggplot(aes(x = tissue, y = exp(mn_expr))) +
geom_point(data = Probe_TOP3A, aes(x=tissue, y=exp(expression)), size=1) +
geom_errorbar(aes(x = tissue,
ymax = exp(top),
ymin =exp(bottom)), width=0.5) +
theme(axis.text.x = element_text(angle = 30, hjust=1))
18.12 Probing for a probe
# customize a user-defined function called `r2`
r2 <- function(data) {
mosaic::rsquared(lm(data$expression ~ data$tissue))
}
ProbeR2 <-
Narrow %>%
group_by(Probe) %>%
dplyr::do(probe_rsq = r2(.)) %>%
mutate(r2 = unlist(probe_rsq))
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ProbeR2 %>%
arrange(desc(r2)) %>%
head(30) 18.6
Actual %>%
ggplot(aes(x = reorder(Probe, desc(r2)), y = r2)) +
geom_point() +
xlab("Probe") +
ylab("R-squared") +
ggtitle("Probes with large R-squared for expression level explained by tissue type") +
theme(axis.text.x = element_text(angle = 45, hjust=1))
Choose one probe with high R-squared
CD53<-
Narrow %>%
filter(Probe=="CD53")
SummaryStats %>%
ggplot(aes(x = tissue, y = exp(mn_expr))) +
geom_point(data = CD53, aes(x=tissue, y=exp(expression))) +
geom_errorbar(aes(x = tissue,
ymax = exp(top),
ymin =exp(bottom)), width=0.5) +
theme(axis.text.x = element_text(angle = 30, hjust=1))
There is a difference between the graph of high R-squared probe and figure 18.5. Most plots are in the confidence interval in 18.5, and the values are larger, high R-squared probe is closed to 0, and intervals are smaller.
18.13 False discoveries
NullR2 <-
Narrow %>%
group_by(Probe) %>%
mutate(expression = mosaic::shuffle(expression)) %>%
group_by(Probe) %>%
do(r2 = r2(.)) %>%
mutate(r2 = unlist(r2))
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ProbeR2 %>%
ggplot(aes(x = r2)) +
geom_density(fill="gray30", color=NA) +
geom_density(data=NullR2, aes(x=r2),
fill="gray80", alpha=.75, color=NA)
18.8
Null <-
NullR2 %>%
arrange(desc(r2)) %>%
head(30)
# append the 30 highest `Null` values to the `Actual` data
Actual$null <- Null$r2
Actual %>%
ggplot(aes(x = reorder(Probe, desc(r2)), y = r2)) +
geom_point() +
geom_point(aes(y = null), color="gray50") +
theme(axis.text.x = element_text(angle = 45, hjust=1))
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